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Found 32 papers, 8 papers with code
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
To overcome this challenge, we used a classifier to instruct the MCMC-based models where and how to refine the candidate sentences.
Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities
Our proposed framework creates 1x1 convolution layers to strengthen the learning of cross-feature spatial interaction between air pollution and important urban dynamic features, particularly road density, building density/height, and street canyon effect.
On the Sparsity of Neural Machine Translation Models
Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources.
Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance.
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each domain in a unified model.
Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying.
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations
Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings.
Meta-Learning for Low-Resource Neural Machine Translation
We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks.
A Stable and Effective Learning Strategy for Trainable Greedy Decoding
Beam search is a widely used approximate search strategy for neural network decoders, and it generally outperforms simple greedy decoding on tasks like machine translation.
Universal Neural Machine Translation for Extremely Low Resource Languages
Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language.
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game
While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains.
Non-Autoregressive Neural Machine Translation
Existing approaches to neural machine translation condition each output word on previously generated outputs.
Ranked #3 on
Machine Translation
on IWSLT2015 English-German
Neural Machine Translation with Gumbel-Greedy Decoding
Previous neural machine translation models used some heuristic search algorithms (e. g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time.
Search Engine Guided Non-Parametric Neural Machine Translation
In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training.
A Teacher-Student Framework for Zero-Resource Neural Machine Translation
While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains.
Trainable Greedy Decoding for Neural Machine Translation
Instead of trying to build a new decoding algorithm for any specific decoding objective, we propose the idea of trainable decoding algorithm in which we train a decoding algorithm to find a translation that maximizes an arbitrary decoding objective.
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}.
Learning to Translate in Real-time with Neural Machine Translation
Translating in real-time, a. k. a.
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.
A Revisit of Infinite Population Models for Evolutionary Algorithms on Continuous Optimization Problems
Then, in order to analyze infinite population models, we build an analytical framework based on convergence in distribution of random elements which take values in the metric space of infinite sequences.
A Social Spider Algorithm for Solving the Non-convex Economic Load Dispatch Problem
Economic Load Dispatch (ELD) is one of the essential components in power system control and operation.
Adaptive Chemical Reaction Optimization for Global Numerical Optimization
A newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains.
Parameter Sensitivity Analysis of Social Spider Algorithm
Social Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems.
Efficient Learning for Undirected Topic Models
Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents.
A Social Spider Algorithm for Global Optimization
Inspired by the social spiders, we propose a novel Social Spider Algorithm to solve global optimization problems.
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
However, the functionality of the inter-molecular ineffective collision operator in the canonical CRO design overlaps that of the on-wall ineffective collision operator, which can potential impair the overall performance.
Sensor Deployment for Air Pollution Monitoring Using Public Transportation System
Air pollution monitoring is a very popular research topic and many monitoring systems have been developed.
Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization
An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid.
Real-Coded Chemical Reaction Optimization with Different Perturbation Functions
The distributions are tested by a set of well-known benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function.
Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization
Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them.
Chemical Reaction Optimization for the Set Covering Problem
The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications.
